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Data Offloading Capacity in a Megalopolis using Taxis and Buses as Data Carriers
Abstract
All means will be welcome to help data flow across smart cities. As a matter of fact, smart cities widely rely in sensing the environment, an action that is prone to generating huge amounts of data. A major challenge is how to collect such data in an efficient way without the need to deploy, whenever necessary, extra (costly) cellular infrastructure. In this paper, we examine the possibility of creating a delay-tolerant vehicular network in the city of Rio de Janeiro, Brazil, using the public transportation system as a data carrier. We evaluate the capacity of such a network by analyzing a large mobility dataset reporting GPS positions of 12,456 buses and 5,833 taxis during a 24-hour period. Our results confirm the viability of the approach and reveal that hundreds of Terabytes can circulate across the city on a daily basis while achieving significant city coverage.
... For the routes of vehicles, we employ the Seattle bus trace [15], which has been used in several recent studies [6,28,30]. The trace shows the actual movement data of about 1200 buses on their normal routes in Seattle, USA for several weeks, a sampled map from the dataset from 08:00 a.m to 08:10 p.m on October 31th, 2003 for our experiment. ...
Internet of Things technology was introduced to allow many physical devices to connect over the Internet. The data and tasks generated by these devices put pressure on the traditional cloud due to high resource and latency demand. Vehicular Fog Computing (VFC) is a concept that utilizes the computational resources integrated into the vehicles to support the processing of end-user-generated tasks. This research first proposes a bag of tasks offloading framework that allows vehicles to handle multiple tasks and any given time step. We then implement an evolution-based algorithm called Time-Cost-aware Task-Node Mapping (TCaTNM) to optimize completion time and operating costs simultaneously. The proposed algorithm is evaluated on datasets of different tasks and computing node sizes. The results show that our scheduling algorithm can save more than $60\%$ of monetary cost than the Particle Swarm Optimization (PSO) algorithm with competitive computation time. Further evaluations also show that our algorithm has a much faster learning rate and can scale its performance as the number of tasks and computing nodes increases.
... In another work, Dias et al. [46] worked on the data offloading through public transport busses and taxis. A gridbased statistical information grid (STING) clustering algorithm is used for clustering analysis with low complexity. ...
p>The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks' traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading's category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.</p
... In another work, Dias et al. [46] worked on the data offloading through public transport busses and taxis. A gridbased statistical information grid (STING) clustering algorithm is used for clustering analysis with low complexity. ...
p>The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks' traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading's category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.</p
... The results of the study indicated that with 120 vehicles on average, 80% coverage can be achieved in less than 24 h. A one-month portion of gathered taxi mobility traces is publicly available at CRAWDAD repository [51] Similarly, Dias et al. [52] investigated the feasibility of a delay-tolerant vehicle network in the city of Rio de Janeiro, Brazil, using public transportation system data. The performance of such a network was evaluated by analyzing a large dataset of high mobility data-12,456 buses and 5833 taxi cabs recorded over a 24 h period based on their GPS positions. ...
Epidemics and pandemics dramatically affect mobility trends around the world, which we have witnessed recently and expect more of in the future. A global energy crisis is looming ahead on the horizon and will redefine the transportation and energy usage patterns, in particular in large cities and metropolitan areas. As the trend continues to expand, the need to efficiently monitor and manage smart city infrastructure, public transportation, service vehicles, and commercial fleets has become of higher importance. This, in turn, requires new methods for dissemination, collection, and processing of data from massive number of already deployed sensing devices. In order to transmit these data efficiently, it is necessary to optimize the connection structure in wireless networks. Emerging open access to real data from different types of networked and sensing devices should be leveraged. It enables construction of models based on frequently updated real data rather than synthetic models or test environments. Hence, the main objective of this article is to introduce the concept of network modeling based on publicly available geographic location data of heterogeneous nodes and to promote the use of real-life diverse open data sources as the basis of novel research related to urban sensor networks. The feasibility of designed modeling architecture is discussed and proved with numerous examples of modeled spatial and spatiotemporal graphs, which are essential in opportunistic routing-related studies using the methods which rely on graph theory. This approach has not been considered before in similar studies and in the literature.
... For MVNs, we use the Seattle bus trace [43], which was also adopted in some other recent researches [29,44,45]. The Seattle bus trace includes the actual movement of approximately 1200 city buses on their normal routes in Seattle, Washington metropolitan area, USA for several weeks. ...
The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.
The optimization of network topology is crucial to achieve efficient data transmission in wireless sensor networks. Recently it has been proven that emerging open data sources can be used for modeling the structures of heterogeneous urban sensor networks. With this, leveraging real location data of various networked and sensing devices became feasible and essential. This approach enables the construction and analysis of more accurate representations based on frequently updated actual network infrastructure topology data, as opposed to using synthetic models or test environments. The presented modeling method serves as the basis for the designed architecture and implemented research environment. This paper introduces a set of algorithms which transform devices’ location data into graph-based wireless network connectivity models. Each algorithm is thoroughly discussed and evaluated. Moreover, static (momentary) and dynamic (time-spanning) network topologies are constructed in four large Polish cities based on publicly available data. Multidimensional simulation-based analysis is conducted to investigate the characteristics of the modeled structures. Directions for further research are suggested as well.
The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks’ traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading’s category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.
The mega-city region was projected as the future outcome of the urbanization process, which is commonly composed of single or multiple main cities and peripheral cities through highly developed transportation networks. Because passenger and freight systems commonly share a physical transport network, the effects of urban morphology changes act on both systems at the same time. Although passenger and freight transport in mega-city regions have been addressed in a growing number of journal publications, an overview of research focusing on both topics together is still lacking. Meanwhile, the potential crossed topics between both systems in mega-city regions were limited in the discussion. This situation may hinder local authorities in the formulation of an appropriate transportation agenda for the promotion of collaboration among the transport system between different cities in mega-city regions, even further aggravate the problem of land-use conflict between city and transport, the unreasonably distributed transport infrastructure, and the excessive allocation of transportation resources, etc. Therefore, it's essential to understand the state-of-the-art in the field of passenger and freight transport in the mega-city regions, and to further analyze the potential crossed topics between the two topics. To this end, this paper adopts a systematic literature review method, covering 468 papers and research works published from 2016 to 2021. The cross-relevance analysis shows that, thus far, only seven crossed topics have covered passenger and freight transport simultaneously, while five potential topics can be further studied. Finally, four research gaps have been identified to which scholars could be encouraged to contribute.
In recent years, there has been a big data revolution in smart cities dues to multiple disciplines such as smart healthcare, smart transportation, and smart community. However, most services in these areas of smart cities have become data-driven, thus generating big data that require sharing, storing, processing, and analysis, which ultimately consumes massive amounts of energy. The accumulation process of these data from different areas of a smart city is a challenging issue. Therefore, researchers have started aiming at the Internet of vehicles (IoV), in which smart vehicles are equipped with computing and storage capabilities to communicate with surrounding infrastructure. In this paper, we propose a subcategory of IoV as the Internet of buses (IoB), where public buses enable a service as a data carrier in a smart city by introducing a neural network-based sustainable data dissemination system (NESUDA), where opportunistic sensing comprises delay-tolerant data collection, processing and disseminating from one place to another place around the city. The objective was to use public transport to carry data from one place to another and to reduce the traffic from traditional networks and energy consumption. An advanced neural network (NN) algorithm was applied to locate the realistic arrival time of public buses for data allocation. We used the Auckland transport (AT) buses data set from the transport agency to validate our model for the level of accuracy in predicted bus arrival time and scheduled arrival time to disseminate data using bus services. Data were uploaded onto buses as per their dwelling time at each stop and terminals within the coverage area of deployed RSU. The offloading capacity of our proposed data dissemination system showed that it could be utilized to effectively complement traditional data networks. Moreover, the maximum offloading capacity at each parent stop could reach up to 360 GB with a huge saving of energy consumption.
Embedding sensors in urban buses is a promising strategy to deploy city-wide wireless sensor networks. By taking advantage of the mobility of buses, it is possible to achieve extended spatial coverage with fewer sensors as compared to a static setup. The trade-offs are that urban buses only cover part of the city, and that the frequency of the buses, and consequently of the data collection, is inhomogeneous across the city. Depending on the communication technology, buses may be unable to deliver the collected data on time. In this paper, we propose a coverage metric that takes into account the delivery delays of sensed data and the frequency at which a given region is sensed. The metric indicates, for a given time window, the proportion of streets that can be sensed under the requirements of an application. We apply the metric to more than 19 million GPS coordinates of 5,706 buses in Rio de Janeiro, mapping the coverage achieved for different application needs during a week. We build an abacus relating the coverage to different application requirements. We also calculate the coverage of a scenario only with static sensors. We show, among several observations, that bus-based sensing increases the coverage of applications by up to 7.6 times, in the worst case analyzed, when compared to a pure static scenario.
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